1 Where Do People Drink The Most Beer, Wine And Spirits?

Back in 2014, fivethiryeight.com published an article on alchohol consumption in different countries. The data drinks is available as part of the fivethirtyeight package. Make sure you have installed the fivethirtyeight package before proceeding.

library(fivethirtyeight)
data(drinks)

What are the variable types? Any missing values we should worry about?

There is 1 character variable and 4 numeric variables. No missing values to worry about.

drinks %>% skim #Skim the data for variable types and missing values.

Data summary
Name	Piped data
Number of rows	193
Number of columns	5
_______________________
Column type frequency:
character	1
numeric	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
country	0	1	3	28	0	193	0

Variable type: numeric

skim_variable	complete_rate	mean	sd	p25	p50	p75	p100	hist
beer_servings	1	106.16	101.14	20.0	76.0	188.0	376.0	▇▃▂▂▁
spirit_servings	1	80.99	88.28	4.0	56.0	128.0	438.0	▇▃▂▁▁
wine_servings	1	49.45	79.70	1.0	8.0	59.0	370.0	▇▁▁▁▁
total_litres_of_pure_alcohol	1	4.72	3.77	1.3	4.2	7.2	14.4	▇▃▅▃▁

There is 1 character variable and 4 numeric variables. No missing values to worry about.

Make a plot that shows the top 25 beer consuming countries

#Create a plot to show the top 25 beer consuming countries
drinks %>%
  slice_max(order_by=beer_servings, n=25) %>%  #Select the top 25 beer consuming countries data
  ggplot(aes(x=beer_servings, y=reorder(country,beer_servings))) + # Plot the data, descending order
  geom_col() +
  theme_bw() +
  labs(title='Beer Consumption by Country', subtitle= 'Top 25', x='Servings (per person)', y='Country') + 
  NULL

Make a plot that shows the top 25 wine consuming countries

#Create a plot to show the top 25 wine consuming countries
drinks %>%
  slice_max(order_by=wine_servings, n=25) %>% #Select the top 25 wine consuming countries
  ggplot(aes(x=wine_servings, y=reorder(country, wine_servings)))+ # Plot the data, descending order
  geom_col() +
  theme_bw() +
  labs(title='Wine Consumption by Country',subtitle= 'Top 25', x='Servings (per person)', y='Country') + 
  NULL

Finally, make a plot that shows the top 25 spirit consuming countries

#Create a plot to show the top 25 spirit consuming countries
drinks %>%
  slice_max(order_by=spirit_servings, n=25) %>% #Select the top 25 spirit consuming countries
  ggplot(aes(x=spirit_servings, y=reorder(country,spirit_servings))) + # Plot the data, descending order
  geom_col() +
  theme_bw() +
  labs(title='Spirit Consumption by Country', subtitle= 'Top 25', x='Servings (per person)', y='Country') + 
  NULL

What can you infer from these plots? Don’t just explain what’s in the graph, but speculate or tell a short story (1-2 paragraphs max).

TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.

Rudimental factors that affect consumption of beer, wine, and spirit include production levels, drinking age limit, pricing, and culture.

Based on the data, it can be observed that beer consumption is the highest for Namibia, Czech Republic, Gabon, and Germany. This can perhaps be attributed to the beer triangle in Namibia, low-cost beer in Czech Republic (cheaper than water), and the lowest drinking age in Germany (16 as opposed to 18 for other countries) (Kohli 2021, Nugent 2021) .

As for wine consumption, France (370 wine servings), Portugal, Andorra, and Switzerland top the list while Ireland is in position 25, at 150 servings. The distribution of the 25 countries seems to have high variance.

Lastly, similar to the wine consumption distribution, spirit consumption also appears to have high variance. While Grenada has a high consumption of spirits perhaps due to culture (women have higher per capita consumption than men) and Belarus due to the relaxed policies, Mongolia may have lower consumption as they prefer their regional fermented milk alcohol drinks (OECD 2020).

2 Analysis of movies- IMDB dataset

We will look at a subset sample of movies, taken from the Kaggle IMDB 5000 movie dataset

movies <- read_csv(here::here("data", "movies.csv"))
glimpse(movies)

## Rows: 2,961
## Columns: 11
## $ title               <chr> "Avatar", "Titanic", "Jurassic World", "The Avenge…
## $ genre               <chr> "Action", "Drama", "Action", "Action", "Action", "…
## $ director            <chr> "James Cameron", "James Cameron", "Colin Trevorrow…
## $ year                <dbl> 2009, 1997, 2015, 2012, 2008, 1999, 1977, 2015, 20…
## $ duration            <dbl> 178, 194, 124, 173, 152, 136, 125, 141, 164, 93, 1…
## $ gross               <dbl> 7.61e+08, 6.59e+08, 6.52e+08, 6.23e+08, 5.33e+08, …
## $ budget              <dbl> 2.37e+08, 2.00e+08, 1.50e+08, 2.20e+08, 1.85e+08, …
## $ cast_facebook_likes <dbl> 4834, 45223, 8458, 87697, 57802, 37723, 13485, 920…
## $ votes               <dbl> 886204, 793059, 418214, 995415, 1676169, 534658, 9…
## $ reviews             <dbl> 3777, 2843, 1934, 2425, 5312, 3917, 1752, 1752, 35…
## $ rating              <dbl> 7.9, 7.7, 7.0, 8.1, 9.0, 6.5, 8.7, 7.5, 8.5, 7.2, …

Besides the obvious variables of title, genre, director, year, and duration, the rest of the variables are as follows:

gross : The gross earnings in the US box office, not adjusted for inflation
budget: The movie’s budget
cast_facebook_likes: the number of facebook likes cast memebrs received
votes: the number of people who voted for (or rated) the movie in IMDB
reviews: the number of reviews for that movie
rating: IMDB average rating

2.1 Use your data import, inspection, and cleaning skills to answer the following:

Are there any missing values (NAs)? Are all entries distinct or are there duplicate entries?

paste('Missing values:', sum(is.na(movies))) #Check missing values

## [1] "Missing values: 0"

paste('Duplicate values:', sum(duplicated(movies))) #Check duplicated values

## [1] "Duplicate values: 0"

Produce a table with the count of movies by genre, ranked in descending order

movies %>%
  group_by(genre) %>%
  count(sort=T)

## # A tibble: 17 × 2
## # Groups:   genre [17]
##    genre           n
##    <chr>       <int>
##  1 Comedy        848
##  2 Action        738
##  3 Drama         498
##  4 Adventure     288
##  5 Crime         202
##  6 Biography     135
##  7 Horror        131
##  8 Animation      35
##  9 Fantasy        28
## 10 Documentary    25
## 11 Mystery        16
## 12 Sci-Fi          7
## 13 Family          3
## 14 Musical         2
## 15 Romance         2
## 16 Western         2
## 17 Thriller        1

Produce a table with the average gross earning and budget (gross and budget) by genre. Calculate a variable return_on_budget which shows how many $ did a movie make at the box office for each $ of its budget. Ranked genres by this return_on_budget in descending order

#Create a table with the average gross earning and budget (`gross` and `budget`) by genre
movies %>%
  group_by(genre) %>% #Group data by genre
  summarise(
    meanGross=mean(gross),
    meanBudget=mean(budget)) %>%
  mutate(return_on_budget=(meanGross-meanBudget)/meanBudget,
         across(return_on_budget, round, 2)) %>% #Calculate return on budget data, round to 2 decimal places
  arrange(-return_on_budget) #Ranked genres by return on budget in descending order

## # A tibble: 17 × 4
##    genre        meanGross meanBudget return_on_budget
##    <chr>            <dbl>      <dbl>            <dbl>
##  1 Musical      92084000    3189500             27.9 
##  2 Family      149160478.  14833333.             9.06
##  3 Western      20821884    3465000              5.01
##  4 Documentary  17353973.   5887852.             1.95
##  5 Horror       37713738.  13504916.             1.79
##  6 Fantasy      42408841.  17582143.             1.41
##  7 Comedy       42630552.  24446319.             0.74
##  8 Mystery      67533021.  39218750              0.72
##  9 Animation    98433792.  61701429.             0.6 
## 10 Biography    45201805.  28543696.             0.58
## 11 Adventure    95794257.  66290069.             0.45
## 12 Drama        37465371.  26242933.             0.43
## 13 Crime        37502397.  26596169.             0.41
## 14 Romance      31264848.  25107500              0.25
## 15 Action       86583860.  71354888.             0.21
## 16 Sci-Fi       29788371.  27607143.             0.08
## 17 Thriller         2468     300000             -0.99

Produce a table that shows the top 15 directors who have created the highest gross revenue in the box office. Don’t just show the total gross amount, but also the mean, median, and standard deviation per director.

#Produce a table that shows the top 15 directors who have created the highest gross revenue in the box office
movies %>%
  group_by(director) %>% #Group data by directors
  summarise(
    total=sum(gross),
    mean=mean(gross),
    median=median(gross),
    sd=sd(gross)) %>% #Calculate mean,median,sd per director
  slice_max(order_by=total, n=15) #Select the top 15 highest gross revenue data, order by total

## # A tibble: 15 × 5
##    director               total       mean     median         sd
##    <chr>                  <dbl>      <dbl>      <dbl>      <dbl>
##  1 Steven Spielberg  4014061704 174524422. 164435221  101421051.
##  2 Michael Bay       2231242537 171634041. 138396624  127161579.
##  3 Tim Burton        2071275480 129454718.  76519172  108726924.
##  4 Sam Raimi         2014600898 201460090. 234903076  162126632.
##  5 James Cameron     1909725910 318287652. 175562880. 309171337.
##  6 Christopher Nolan 1813227576 226653447  196667606. 187224133.
##  7 George Lucas      1741418480 348283696  380262555  146193880.
##  8 Robert Zemeckis   1619309108 124562239. 100853835   91300279.
##  9 Clint Eastwood    1378321100  72543216.  46700000   75487408.
## 10 Francis Lawrence  1358501971 271700394. 281666058  135437020.
## 11 Ron Howard        1335988092 111332341  101587923   81933761.
## 12 Gore Verbinski    1329600995 189942999. 123207194  154473822.
## 13 Andrew Adamson    1137446920 284361730  279680930. 120895765.
## 14 Shawn Levy        1129750988 102704635.  85463309   65484773.
## 15 Ridley Scott      1128857598  80632686.  47775715   68812285.

Finally, ratings. Produce a table that describes how ratings are distributed by genre. We don’t want just the mean, but also, min, max, median, SD and some kind of a histogram or density graph that visually shows how ratings are distributed.

#Calculate median,min,max,and sd of ratings
movies %>%
  group_by(genre) %>% #Group data by genre
  summarise(
    mean=mean(rating),
    min=min(rating),
    max=max(rating),
    median=median(rating),
    sd=sd(rating)) %>% 
  mutate(across(2:6, round, 2)) # round columns 2 through 6 to 2 decimal places

## # A tibble: 17 × 6
##    genre        mean   min   max median    sd
##    <chr>       <dbl> <dbl> <dbl>  <dbl> <dbl>
##  1 Action       6.23   2.1   9     6.3   1.03
##  2 Adventure    6.51   2.3   8.6   6.6   1.09
##  3 Animation    6.65   4.5   8     6.9   0.97
##  4 Biography    7.11   4.5   8.9   7.2   0.76
##  5 Comedy       6.11   1.9   8.8   6.2   1.02
##  6 Crime        6.92   4.8   9.3   6.9   0.85
##  7 Documentary  6.66   1.6   8.5   7.4   1.77
##  8 Drama        6.73   2.1   8.8   6.8   0.92
##  9 Family       6.5    5.7   7.9   5.9   1.22
## 10 Fantasy      6.15   4.3   7.9   6.45  0.96
## 11 Horror       5.83   3.6   8.5   5.9   1.01
## 12 Musical      6.75   6.3   7.2   6.75  0.64
## 13 Mystery      6.86   4.6   8.5   6.9   0.88
## 14 Romance      6.65   6.2   7.1   6.65  0.64
## 15 Sci-Fi       6.66   5     8.2   6.4   1.09
## 16 Thriller     4.8    4.8   4.8   4.8  NA   
## 17 Western      5.7    4.1   7.3   5.7   2.26

#Create histogram that shows how ratings are distributed by genre
movies %>%
  ggplot(aes(x=rating)) +
  geom_histogram(binwidth=0.5) +
  facet_wrap(~genre) +
  theme_bw() +
  labs(title='Rating distribution by genre', x='Rating', y='Number of movies') + 
  NULL

2.2 Use `ggplot` to answer the following

Examine the relationship between gross and cast_facebook_likes. Produce a scatterplot and write one sentence discussing whether the number of facebook likes that the cast has received is likely to be a good predictor of how much money a movie will make at the box office. What variable are you going to map to the Y- and X- axes?

#Create scatterplot to examine the relationship between gross and cast_facebook_likes
movies %>%
  ggplot(aes(x=cast_facebook_likes, y=gross, alpha=.01)) + #Make the points opaque to see where there are denser areas
  geom_point() +
  geom_smooth() +
  scale_x_log10() + #log the x & y axes to see a clearer snapshot of what is occurring 
  scale_y_log10() +
  theme_bw() +
  labs(title='Relationship Between Gross Profit and Facebook Likes', x='Facebook Likes', y='Gross Profit') + 
  theme(legend.position = "none") +
  NULL

TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.

The x-axis depicts the Facebook likes and the y-axis gross profits. While the data show a slight positive correlation between Facebook Likes and Gross Profit, there is immense variability, so Facebook likes do not seem to be a strong predictor of the money a movie makes. Mapping other variables could help determine what truly impacts the money a movie makes.

Examine the relationship between gross and budget. Produce a scatterplot and write one sentence discussing whether budget is likely to be a good predictor of how much money a movie will make at the box office.

#Create scatterplot to examine the relationship between gross and budget
movies %>%
  ggplot(aes(x=budget, y=gross, alpha=.01)) + #Make the points opaque to see where there are denser areas
  geom_point() +
  geom_smooth() +
  theme_bw() +
  labs(title='Relationship Between Gross Profit and Budget', x='Budget', y='Gross Profit') +
  theme(legend.position = "none") +
  NULL

TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.

Compared to the previous plot, this plot shows a higher correlation between budget and gross profit; however, there is still much variability and we cannot conclude that the relationship is significant.

Examine the relationship between gross and rating. Produce a scatterplot, faceted by genre and discuss whether IMDB ratings are likely to be a good predictor of how much money a movie will make at the box office. Is there anything strange in this dataset?

#Create scatterplot to examine the relationship between gross and ratings, faceted by genre
movies %>%
  ggplot(aes(x=rating, y=gross)) +
  geom_point() +
  facet_wrap(~genre) +
  theme_bw() +
  labs(title='Relationship Between Gross Profit and Rating', subtitle= 'By Genre', x='Rating', y='Gross Profit') + 
  NULL

Our hypothesis before examining the data was that rating would be a good predictor of gross profit. While we see that is the case for Action, Adventure, and perhaps Drama, there are other genres that did not align with our hypothesis. Documentaries, for example, do not see a gross profit of greater than 200 mil but they do have high ratings. This makes sense because while Documentary films tend to have high ratings due to the realness of their topics, they are not popular films to see in the theaters.

There are irregularities in the dataset that these plots depict. Firstly, it seems that the dataset is highly imbalanced as there are very few data points for family, musical, romance, thriller, and western films. Next, it looks as though some genres have low gross profits compared to others, when historically those genres actually produce highly. For example, the Star Wars films are one of the highest grossing Sci-Fi films, and films in general, but the data do not reflect this. This may be due to the data only assigning one genre to a movie, so a movie like Star Wars would be assigned Action, rather than Sci-Fi.

3 Returns of financial stocks

You may find useful the material on finance data sources.

We will use the tidyquant package to download historical data of stock prices, calculate returns, and examine the distribution of returns.

We must first identify which stocks we want to download data for, and for this we must know their ticker symbol; Apple is known as AAPL, Microsoft as MSFT, McDonald’s as MCD, etc. The file nyse.csv contains 508 stocks listed on the NYSE, their ticker symbol, name, the IPO (Initial Public Offering) year, and the sector and industry the company is in.

nyse <- read_csv(here::here("data","nyse.csv"))
glimpse(nyse)

## Rows: 508
## Columns: 6
## $ symbol        <chr> "MMM", "ABB", "ABT", "ABBV", "ACN", "AAP", "AFL", "A", "…
## $ name          <chr> "3M Company", "ABB Ltd", "Abbott Laboratories", "AbbVie …
## $ ipo_year      <chr> "n/a", "n/a", "n/a", "2012", "2001", "n/a", "n/a", "1999…
## $ sector        <chr> "Health Care", "Consumer Durables", "Health Care", "Heal…
## $ industry      <chr> "Medical/Dental Instruments", "Electrical Products", "Ma…
## $ summary_quote <chr> "https://www.nasdaq.com/symbol/mmm", "https://www.nasdaq…

Based on this dataset, create a table and a bar plot that shows the number of companies per sector, in descending order

#Create table for numbers of companies per sector in descending order
nyse %>%
  group_by(sector) %>%
  count(sort=T)

## # A tibble: 12 × 2
## # Groups:   sector [12]
##    sector                    n
##    <chr>                 <int>
##  1 Finance                  97
##  2 Consumer Services        79
##  3 Public Utilities         60
##  4 Capital Goods            45
##  5 Health Care              45
##  6 Energy                   42
##  7 Technology               40
##  8 Basic Industries         39
##  9 Consumer Non-Durables    31
## 10 Miscellaneous            12
## 11 Transportation           10
## 12 Consumer Durables         8

#Create bar plot for numbers of companies per sector in descending order
nyse %>%
  group_by(sector) %>%
  count(sort=T) %>%
  ggplot(aes(x=n, y=fct_reorder(sector, n))) +
  geom_col() +
  theme_bw() +
  labs(title='Number of Stocks By Sector', x='Number of Stocks', y='Sector') + 
  NULL

Next, let’s choose some stocks and their ticker symbols and download some data. You MUST choose 6 different stocks from the ones listed below; You should, however, add SPY which is the SP500 ETF (Exchange Traded Fund).

myStocks <- c("AAPL","JPM","DIS","DPZ","ANF","SPY") %>%
  tq_get(get  = "stock.prices",
         from = "2011-01-01",
         to   = "2021-08-31") %>%
  group_by(symbol) 

glimpse(myStocks)

## Rows: 16,098
## Columns: 8
## Groups: symbol [6]
## $ symbol   <chr> "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL…
## $ date     <date> 2011-01-03, 2011-01-04, 2011-01-05, 2011-01-06, 2011-01-07, …
## $ open     <dbl> 11.6, 11.9, 11.8, 12.0, 11.9, 12.1, 12.3, 12.3, 12.3, 12.4, 1…
## $ high     <dbl> 11.8, 11.9, 11.9, 12.0, 12.0, 12.3, 12.3, 12.3, 12.4, 12.4, 1…
## $ low      <dbl> 11.6, 11.7, 11.8, 11.9, 11.9, 12.0, 12.1, 12.2, 12.3, 12.3, 1…
## $ close    <dbl> 11.8, 11.8, 11.9, 11.9, 12.0, 12.2, 12.2, 12.3, 12.3, 12.4, 1…
## $ volume   <dbl> 4.45e+08, 3.09e+08, 2.56e+08, 3.00e+08, 3.12e+08, 4.49e+08, 4…
## $ adjusted <dbl> 10.1, 10.2, 10.2, 10.2, 10.3, 10.5, 10.5, 10.6, 10.6, 10.7, 1…

Financial performance analysis depend on returns; If I buy a stock today for 100 and I sell it tomorrow for 101.75, my one-day return, assuming no transaction costs, is 1.75%. So given the adjusted closing prices, our first step is to calculate daily and monthly returns.

#calculate daily returns
myStocks_returns_daily <- myStocks %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "daily", 
               type       = "log",
               col_rename = "daily_returns",
               cols = c(nested.col))  

#calculate monthly  returns
myStocks_returns_monthly <- myStocks %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "monthly", 
               type       = "arithmetic",
               col_rename = "monthly_returns",
               cols = c(nested.col)) 

#calculate yearly returns
myStocks_returns_annual <- myStocks %>%
  group_by(symbol) %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "yearly", 
               type       = "arithmetic",
               col_rename = "yearly_returns",
               cols = c(nested.col))

Create a table where you summarise monthly returns for each of the stocks and SPY; min, max, median, mean, SD.

#Create a table that summarise min, max, median, mean, SD of monthly returns for each of the stock
myStocks_returns_monthly %>%
  group_by(symbol) %>%
  summarise(
    min=min(monthly_returns),
    max=max(monthly_returns),
    median=median(monthly_returns),
    mean=mean(monthly_returns),
    sd=sd(monthly_returns)) %>% 
  mutate(across(2:6, round, 3)) #round for easier understanding

## # A tibble: 6 × 6
##   symbol    min   max median  mean    sd
##   <chr>   <dbl> <dbl>  <dbl> <dbl> <dbl>
## 1 AAPL   -0.181 0.217  0.026 0.024 0.078
## 2 ANF    -0.421 0.507  0.003 0.009 0.145
## 3 DIS    -0.179 0.234  0.009 0.016 0.068
## 4 DPZ    -0.188 0.342  0.025 0.031 0.075
## 5 JPM    -0.229 0.202  0.022 0.015 0.072
## 6 SPY    -0.125 0.127  0.017 0.012 0.038

Plot a density plot, using geom_density(), for each of the stocks

#Create a density plot for each stocks
myStocks_returns_monthly %>%
  ggplot(aes(x=monthly_returns, colour=symbol, fill=symbol)) + 
  geom_density(alpha=0.3) +
  facet_wrap(~symbol) +
  theme_bw() +
  theme(legend.position='none') +
  labs(title='Density Plot of Monthly Returns', x='Return', y='Density') +
  NULL

What can you infer from this plot? Which stock is the riskiest? The least risky?

TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.

Plotting the monthly returns for the stocks of various companies over time helps us determine the level of risk involved in such investments. According to the density plot, the most risky stock is ANF and the least risky is SPY.

Finally, make a plot that shows the expected monthly return (mean) of a stock on the Y axis and the risk (standard deviation) in the X-axis. Please use ggrepel::geom_text_repel() to label each stock

#Create a plot to identify the expected monthly return(mean) and rish(SD) of each stock
myStocks_returns_monthly %>%
  group_by(symbol) %>%
  summarise(
    sd=sd(monthly_returns),
    mean=mean(monthly_returns)) %>%
  ggplot(aes(x=sd, y=mean, label=symbol)) + #Add the stock symbol as the label for each point
  geom_point() +
  theme_bw() +
  ggrepel::geom_text_repel() + #Label each stock
  labs(title='Risk and Return Plot for Selected Stocks', x='Risk', y='Return') +
  NULL

What can you infer from this plot? Are there any stocks which, while being riskier, do not have a higher expected return?

TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.

This plot depicts the risk-return relationship of each stock. While DPZ provides the highest return for a moderate risk level, ANF, despite being the riskiest, provides the least amount of return.

4 On your own: IBM HR Analytics

For this task, you will analyse a data set on Human Resoruce Analytics. The IBM HR Analytics Employee Attrition & Performance data set is a fictional data set created by IBM data scientists. Among other things, the data set includes employees’ income, their distance from work, their position in the company, their level of education, etc. A full description can be found on the website.

First let us load the data

#Load the data
hr_dataset <- read_csv(here::here("data", "datasets_1067_1925_WA_Fn-UseC_-HR-Employee-Attrition.csv"))
glimpse(hr_dataset)

## Rows: 1,470
## Columns: 35
## $ Age                      <dbl> 41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 2…
## $ Attrition                <chr> "Yes", "No", "Yes", "No", "No", "No", "No", "…
## $ BusinessTravel           <chr> "Travel_Rarely", "Travel_Frequently", "Travel…
## $ DailyRate                <dbl> 1102, 279, 1373, 1392, 591, 1005, 1324, 1358,…
## $ Department               <chr> "Sales", "Research & Development", "Research …
## $ DistanceFromHome         <dbl> 1, 8, 2, 3, 2, 2, 3, 24, 23, 27, 16, 15, 26, …
## $ Education                <dbl> 2, 1, 2, 4, 1, 2, 3, 1, 3, 3, 3, 2, 1, 2, 3, …
## $ EducationField           <chr> "Life Sciences", "Life Sciences", "Other", "L…
## $ EmployeeCount            <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ EmployeeNumber           <dbl> 1, 2, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 16,…
## $ EnvironmentSatisfaction  <dbl> 2, 3, 4, 4, 1, 4, 3, 4, 4, 3, 1, 4, 1, 2, 3, …
## $ Gender                   <chr> "Female", "Male", "Male", "Female", "Male", "…
## $ HourlyRate               <dbl> 94, 61, 92, 56, 40, 79, 81, 67, 44, 94, 84, 4…
## $ JobInvolvement           <dbl> 3, 2, 2, 3, 3, 3, 4, 3, 2, 3, 4, 2, 3, 3, 2, …
## $ JobLevel                 <dbl> 2, 2, 1, 1, 1, 1, 1, 1, 3, 2, 1, 2, 1, 1, 1, …
## $ JobRole                  <chr> "Sales Executive", "Research Scientist", "Lab…
## $ JobSatisfaction          <dbl> 4, 2, 3, 3, 2, 4, 1, 3, 3, 3, 2, 3, 3, 4, 3, …
## $ MaritalStatus            <chr> "Single", "Married", "Single", "Married", "Ma…
## $ MonthlyIncome            <dbl> 5993, 5130, 2090, 2909, 3468, 3068, 2670, 269…
## $ MonthlyRate              <dbl> 19479, 24907, 2396, 23159, 16632, 11864, 9964…
## $ NumCompaniesWorked       <dbl> 8, 1, 6, 1, 9, 0, 4, 1, 0, 6, 0, 0, 1, 0, 5, …
## $ Over18                   <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", …
## $ OverTime                 <chr> "Yes", "No", "Yes", "Yes", "No", "No", "Yes",…
## $ PercentSalaryHike        <dbl> 11, 23, 15, 11, 12, 13, 20, 22, 21, 13, 13, 1…
## $ PerformanceRating        <dbl> 3, 4, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, …
## $ RelationshipSatisfaction <dbl> 1, 4, 2, 3, 4, 3, 1, 2, 2, 2, 3, 4, 4, 3, 2, …
## $ StandardHours            <dbl> 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 8…
## $ StockOptionLevel         <dbl> 0, 1, 0, 0, 1, 0, 3, 1, 0, 2, 1, 0, 1, 1, 0, …
## $ TotalWorkingYears        <dbl> 8, 10, 7, 8, 6, 8, 12, 1, 10, 17, 6, 10, 5, 3…
## $ TrainingTimesLastYear    <dbl> 0, 3, 3, 3, 3, 2, 3, 2, 2, 3, 5, 3, 1, 2, 4, …
## $ WorkLifeBalance          <dbl> 1, 3, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, …
## $ YearsAtCompany           <dbl> 6, 10, 0, 8, 2, 7, 1, 1, 9, 7, 5, 9, 5, 2, 4,…
## $ YearsInCurrentRole       <dbl> 4, 7, 0, 7, 2, 7, 0, 0, 7, 7, 4, 5, 2, 2, 2, …
## $ YearsSinceLastPromotion  <dbl> 0, 1, 0, 3, 2, 3, 0, 0, 1, 7, 0, 0, 4, 1, 0, …
## $ YearsWithCurrManager     <dbl> 5, 7, 0, 0, 2, 6, 0, 0, 8, 7, 3, 8, 3, 2, 3, …

I am going to clean the data set, as variable names are in capital letters, some variables are not really necessary, and some variables, e.g., education are given as a number rather than a more useful description

#Clean the data set
hr_cleaned <- hr_dataset %>%
  clean_names() %>% 
  mutate(
    education = case_when(
      education == 1 ~ "Below College",
      education == 2 ~ "College",
      education == 3 ~ "Bachelor",
      education == 4 ~ "Master",
      education == 5 ~ "Doctor"),
    environment_satisfaction = case_when(
      environment_satisfaction == 1 ~ "Low",
      environment_satisfaction == 2 ~ "Medium",
      environment_satisfaction == 3 ~ "High",
      environment_satisfaction == 4 ~ "Very High"),
    job_satisfaction = case_when(
      job_satisfaction == 1 ~ "Low",
      job_satisfaction == 2 ~ "Medium",
      job_satisfaction == 3 ~ "High",
      job_satisfaction == 4 ~ "Very High"),
    performance_rating = case_when(
      performance_rating == 1 ~ "Low",
      performance_rating == 2 ~ "Good",
      performance_rating == 3 ~ "Excellent",
      performance_rating == 4 ~ "Outstanding"),
    work_life_balance = case_when(
      work_life_balance == 1 ~ "Bad",
      work_life_balance == 2 ~ "Good",
      work_life_balance == 3 ~ "Better",
      work_life_balance == 4 ~ "Best")) %>% 
  select(age, attrition, daily_rate, department,
         distance_from_home, education,
         gender, job_role,environment_satisfaction,
         job_satisfaction, marital_status,
         monthly_income, num_companies_worked, percent_salary_hike,
         performance_rating, total_working_years,
         work_life_balance, years_at_company,
         years_since_last_promotion)
hr_cleaned

## # A tibble: 1,470 × 19
##      age attrition daily_rate department     distance_from_ho… education  gender
##    <dbl> <chr>          <dbl> <chr>                      <dbl> <chr>      <chr> 
##  1    41 Yes             1102 Sales                          1 College    Female
##  2    49 No               279 Research & De…                 8 Below Col… Male  
##  3    37 Yes             1373 Research & De…                 2 College    Male  
##  4    33 No              1392 Research & De…                 3 Master     Female
##  5    27 No               591 Research & De…                 2 Below Col… Male  
##  6    32 No              1005 Research & De…                 2 College    Male  
##  7    59 No              1324 Research & De…                 3 Bachelor   Female
##  8    30 No              1358 Research & De…                24 Below Col… Male  
##  9    38 No               216 Research & De…                23 Bachelor   Male  
## 10    36 No              1299 Research & De…                27 Bachelor   Male  
## # … with 1,460 more rows, and 12 more variables: job_role <chr>,
## #   environment_satisfaction <chr>, job_satisfaction <chr>,
## #   marital_status <chr>, monthly_income <dbl>, num_companies_worked <dbl>,
## #   percent_salary_hike <dbl>, performance_rating <chr>,
## #   total_working_years <dbl>, work_life_balance <chr>, years_at_company <dbl>,
## #   years_since_last_promotion <dbl>

Produce a one-page summary describing this dataset. Here is a non-exhaustive list of questions:

How often do people leave the company (attrition)?

hr_cleaned %>%
  summarize(
    count_yes=count(attrition == 'Yes'), 
    count_total=count(attrition)) %>%
  mutate(proportion=count_yes/count_total, #Find the number of yes attrition/total attrition to find the proportion of people leaving the company
         across(proportion, round, 3)) %>% #round
  select(proportion)

## # A tibble: 1 × 1
##   proportion
##        <dbl>
## 1      0.192

How are age, years_at_company, monthly_income and years_since_last_promotion distributed? Can you roughly guess which of these variables is closer to Normal just by looking at summary statistics?

hr_cleaned %>%
  select(age, years_at_company, monthly_income, years_since_last_promotion) %>%
  summary

##       age       years_at_company monthly_income  years_since_last_promotion
##  Min.   :18.0   Min.   : 0       Min.   : 1009   Min.   : 0.00             
##  1st Qu.:30.0   1st Qu.: 3       1st Qu.: 2911   1st Qu.: 0.00             
##  Median :36.0   Median : 5       Median : 4919   Median : 1.00             
##  Mean   :36.9   Mean   : 7       Mean   : 6503   Mean   : 2.19             
##  3rd Qu.:43.0   3rd Qu.: 9       3rd Qu.: 8379   3rd Qu.: 3.00             
##  Max.   :60.0   Max.   :40       Max.   :19999   Max.   :15.00

How are job_satisfaction and work_life_balance distributed? Don’t just report counts, but express categories as % of total?

#Calculate the total number of employees
total <- nrow(hr_cleaned) 

#Calculate the % of total of each job_satisfaction categories 
hr_cleaned %>%
  group_by(job_satisfaction) %>%
  count() %>%
  mutate(proportion=n/total, 
         across(proportion, round, 3)) %>% #round
  select(proportion)

## # A tibble: 4 × 2
## # Groups:   job_satisfaction [4]
##   job_satisfaction proportion
##   <chr>                 <dbl>
## 1 High                  0.301
## 2 Low                   0.197
## 3 Medium                0.19 
## 4 Very High             0.312

#Calculate the % of total of each work_life_balance categories
hr_cleaned %>%
  group_by(work_life_balance) %>%
  count() %>%
  mutate(proportion=n/total, 
         across(proportion, round, 3)) %>% #round
  select(proportion)

## # A tibble: 4 × 2
## # Groups:   work_life_balance [4]
##   work_life_balance proportion
##   <chr>                  <dbl>
## 1 Bad                    0.054
## 2 Best                   0.104
## 3 Better                 0.607
## 4 Good                   0.234

Is there any relationship between monthly income and education? Monthly income and gender?

#Create a box plot to examine the relationship between monthly income and education
hr_cleaned %>%
  ggplot(aes(x=monthly_income, y=reorder(education, monthly_income))) +
  geom_boxplot() +
  theme_bw() +
  labs(title='Relationship Between Education and Monthly Income', x='Monthly Income', y='Education') +
  NULL

#Create a box plot to examine the relationship between monthly income and gender
hr_cleaned %>%
  ggplot(aes(x=monthly_income, y=reorder(gender, monthly_income))) +
  geom_boxplot() +
  theme_bw() +
  labs(title='Relationship Between Gender and Monthly Income', x='Monthly Income', y='Gender') +
  NULL

Plot a boxplot of income vs job role. Make sure the highest-paid job roles appear first.

#Create a box plot to examine the relationship between monthly income and job role
hr_cleaned %>%
  ggplot(aes(x=monthly_income, y=reorder(job_role, monthly_income))) + #Make the job roles appear in descending order of monthly income
  geom_boxplot() +
  theme_bw() +
  labs(title='Relationship Between Job Role and Monthly Income', x='Monthly Income', y='Job Role') +
  NULL

Calculate and plot a bar chart of the mean (or median?) income by education level.

#Create a bar chart of mean income by education level
hr_cleaned %>%
  group_by(education) %>%
  summarise(mean=mean(monthly_income)) %>% #Calculate the mean income
  ggplot(aes(x=education, y=mean)) +
  geom_col() +
  theme_bw() +
  labs(title='Relationship Between Education and Average Monthly Income', x='Education', y='Average Monthly Income') +
  NULL

Plot the distribution of income by education level. Use a facet_wrap and a theme from ggthemes.

#Create a density plot of monthly income by education level, faceted by education level
hr_cleaned %>%
  ggplot(aes(x=monthly_income, colour=education, fill=education)) +
  geom_density(alpha=0.3) +
  facet_wrap(~education) +
  theme_solid() +
  theme(legend.position='none', text=element_text(size=10)) +
  labs(title='Density Plot of Monthly Income', subtitle='By Education Level', x='Monthly Income', y='Density') +
  NULL

Plot income vs age, faceted by job_role.

#Create a scatter plot if income vs age, facted by job roles
hr_cleaned %>%
  ggplot(aes(y=monthly_income, x=age, color=job_role)) +
  facet_wrap(~job_role) +
  geom_point() +
  theme(legend.position='none') +
  labs(title='Relationship Between Age and Monthly Income', subtitle='By Job Role', x='Monthly Income', y='Age') +
  NULL

TYPE YOUR ANSWER AFTER (AND OUTSIDE!) THIS BLOCKQUOTE.

After analysis of the IBM HR Analytics Employee Attrition & Performance Data, we discovered interesting information that may be useful for senior leaders and HR departments to utilize when making decisions to improve the company.

We looked at statistics regarding employee satisfaction, work-life balance, and attrition. Looking at the proportion of job satisfaction, ranked in terms of Very High, High, Medium, or Low, we found that the percentage of employees that ranked Very High and High job satisfaction is 61.3% which is a substantial value. However, the percentage of employees that ranked Low is quite high at 19.7%. The IBM HR department should investigate and work on ways to improve this value, at least to a Medium. It would also be worthwhile for the company to keep the data year by year and see if/how job satisfaction improves. Next, we looked at the proportion of work-life balance, a natural indicator of job satisfaction, which was ranked in terms of Best, Better, Good, and Bad. The percentage of employees that said bad is very low at 5.44%. However, we believe that since job satisfaction and work-life balance are so closely related, that both should be on the same scale of Very High, High, Medium, or Low. This would make it easier for the HR department to compare the data and it would give employees the opportunity to rank their work-life balance as Medium. Last in relation to employee satisfaction, we looked at employee turnover. We found that the turnover rate is 19.2% which is generally low; however, it is slightly higher compared to the average turnover rate for technology companies in the UK in 2019, which is 18.3% (Munss 2019).

We also looked at plots to compare the relationship between income and other variables, such as education, gender, job role, and age. The relationship between income and education is as expected, with the highest education (doctor) attaining higher income. Then, in descending order, master, bachelor, college, and below college, as expected. However, there are outliers in each of the education levels below doctor that reach high income levels at the same or even higher income than doctor. These outliers are great as they indicate that employees with lower education still have the opportunity to reach high income levels and should be used by the HR department when hiring people of different education levels. Another great observation was the relationship between income and gender which is very similar for males and females, with females achieving slightly higher income than males. This information should be shared when hiring and celebrated within the company. We also looked at the relationship between income and job role which was somewhat as expected. Managers and Research Directors have much higher income than the other roles, as their seniority and responsibilities is greater. Then Healthcare Representatives, Manufacturing Directors, Sales Executives come next with similar incomes, followed by Human Resources, Research Scientists, Laboratory Technicians, and Sales Representatives at the bottom of the income ladder. While the order of income by job role makes sense, there is high variability within each role, which does not make much sense. For employees with the same role, IBM should look at the data and try to lower the variability in income to create higher equality for their employees. Lastly, we looked at the relationship of income and age, grouped by job role. For most job roles, there is a small positive correlation between income and age; however, for Laboratory Technicians, Research Scientists, and Sales Representatives, there is very little. This tells us that income at IBM has more to do with the actual role than the age, which is understandable.

Lastly, we looked at summary statistics about age, income, years at company, and years since last promotion. Based on the summary statistics, we guessed that income and age would follow a distribution similar to a normal distribution because the mean and the median are closely aligned. When we actually looked at the density plots, only one of our hypothesis was correct: age. The other variables are highly skewed right, meaning the majority of IBM has lower income, years at company, and years since last promotion.

5 Challenge 1: Replicating a chart

The purpose of this exercise is to reproduce a plot using your dplyr and ggplot2 skills. Read the article The Racial Factor: There’s 77 Counties Which Are Deep Blue But Also Low-Vaxx. Guess What They Have In Common? and have a look at the attached figure.

You dont have to worry about the blue-red backgound and don’t worry about replicating it exactly, try and see how far you can get. You’re encouraged to work together if you want to and exchange tips/tricks you figured out– and even though the figure in the original article is from early July 2021, you can use the most recent data.

Some hints to get you started:

To get vaccination by county, we will use data from the CDC
You need to get County Presidential Election Returns 2000-2020
Finally, you also need an estimate of the population of each county

The below code creates a graph similar to that on the ACASignups website on Trump supporters and COVID vaccination rates:

#Cleaning the election result data 
results_cleaned <- election2020_results %>%
  filter(candidate == 'DONALD J TRUMP', mode == 'TOTAL') %>% #Filtering for Donald Trump and Mode = Total since total is the default ballot mode in the US
  select(fips, candidatevotes, totalvotes) %>% #Selecting relevant columns 
  mutate(percentage_trump=candidatevotes/totalvotes*100) %>% #Creating a new column for percentage of people who voted for Trump
  select(-candidatevotes, -totalvotes) #Getting rid of unnecessary columns

#Cleaning the vaccination data 
vaccinations_cleaned <- vaccinations %>% 
  filter(date == '07/04/2021') %>% #Filtering for July 4, 2021
  mutate(
    pct_vaccinated=case_when(
      recip_state %in% c('CA', 'GA', 'IA', 'MI', 'TX') ~ administered_dose1_pop_pct,
      T ~ series_complete_pop_pct)) %>% #Taking administered_dose1_pop_pct as the pct_vaccinated for CA, GA, IA, MI, and TX as says in the original plot
  select(fips, pct_vaccinated) %>% #Getting rid of unnecessary columns
  filter(pct_vaccinated > 0.0) #Filtering out pct_vaccinated = 0%

#Merging election result data and vaccination data 
data <- results_cleaned %>%
  inner_join(population, by='fips') %>%
  inner_join(vaccinations_cleaned, by='fips') %>%
  filter()

#Graphing the data (Muster 2021). 
data %>%
  ggplot(aes(x=percentage_trump, y=pct_vaccinated)) +
  geom_point(size=0.5) + #Adjusting point size to 0.5
  geom_point(aes(size=pop_estimate_2019), colour='blue', alpha=0.25) + #Adding circles with size based on population size in 2019 in opaque blue 
  geom_hline(yintercept=85, linetype='dashed', color='black') + #Including Herd Immunity threshold at 85%
  annotate('text', x=23, y=87, label="Herd Immunity threshold (?)") +
  geom_hline(yintercept=53.9, linetype='dashed', color='black') + #Including Target threshold at 53.9% 
  annotate('text', x=15.5, y=55.9, label="Target: 53.9%") +
  geom_hline(yintercept=50.8, linetype='dashed', color='black') + #Including Actual threshold at 50.8% 
  annotate('text', x=15.5, y=52.8, label="Actual: 50.8%") +
  theme_bw() +
  theme(legend.position='none') +
  labs(title='COVID-19 VACCINATION LEVELS OUT OF TOTAL POPULATION BY COUNTY', subtitle='(most states based on FULLY vaccinated only; CA, GA, IA, MI & TX based on total doses administered)', x='2020 Trump Vote %', y='% of Total Population Vaccinated') + #Title and subtitle
  scale_size_continuous(range=c(1, 20)) + #Scaling the graph 
  scale_x_continuous(breaks=seq(0, 100, 5)) +
  scale_y_continuous(breaks=seq(0, 100, 5)) +
  NULL

6 Challenge 2: Opinion polls for the 2021 German elections

The Guardian newspaper has an election poll tracker for the upcoming German election. The list of the opinion polls since Jan 2021 can be found at Wikipedia and the task is to reproduce the graph similar to the one produced by the Guardian.

The following code will scrape the wikipedia page and import the table in a dataframe.

url <- "https://en.wikipedia.org/wiki/Opinion_polling_for_the_2021_German_federal_election"
# https://www.economist.com/graphic-detail/who-will-succeed-angela-merkel
# https://www.theguardian.com/world/2021/jun/21/german-election-poll-tracker-who-will-be-the-next-chancellor

# get tables that exist on wikipedia page
tables <- url %>% 
  read_html() %>% 
  html_nodes(css="table")

# parse HTML tables into a dataframe called polls 
# Use purr::map() to create a list of all tables in URL
polls <- map(tables, . %>% 
             html_table(fill=TRUE)%>% 
             janitor::clean_names())

# list of opinion polls
german_election_polls <- polls[[1]] %>% # the first table on the page contains the list of all opinions polls
  slice(2:(n()-1)) %>%  # drop the first row, as it contains again the variable names and last row that contains 2017 results
  mutate(
         # polls are shown to run from-to, e.g. 9-13 Aug 2021. We keep the last date, 13 Aug here, as the poll date
         # and we extract it by picking the last 11 characters from that field
         end_date = str_sub(fieldwork_date, -11),
         # end_date is still a string, so we convert it into a date object using lubridate::dmy()
         end_date = dmy(end_date),
         # we also get the month and week number from the date, if we want to do analysis by month- week, etc.
         month = month(end_date),
         week = isoweek(end_date))

The below text will produce a graph of the opinion poll similar to that by the Guardian:

#Select only necessary columns of the dataset
german_election_polls_cleaned <- german_election_polls %>%
  select(end_date, union, spd, af_d, fdp, linke, grune)

#Assigning colors to each political party, extracted from original plot using adobe color 
col_union <- 'black'
col_spd <- '#BF0404'
col_af_d <- '#8C1F7A'
col_fdp <- '#F2AE2E'
col_linke <- '#0A5789'
col_grune <- '#45BF41'

#Plotting the election outcomes
plot <- german_election_polls_cleaned %>%
  ggplot +
  geom_point(aes(x=end_date, y=union, colour='black')) + #Union Party points assigned the color black
  geom_smooth(aes(x=end_date, y=union), colour='black', span=0.09, se=F) + #Union Party curve with span = 0.09 assigned the color black
  geom_point(aes(x=end_date, y=spd), colour=col_spd, alpha=0.5) + #SPD Party points assigned the color red
  geom_smooth(aes(x=end_date, y=spd), colour=col_spd, span=0.09, se=F) + #SPD Party curve with span = 0.09 assigned the color red
  geom_point(aes(x=end_date, y=af_d), colour=col_af_d, alpha=0.5) + #AfD Party points assigned the color purple
  geom_smooth(aes(x=end_date, y=af_d), colour=col_af_d, span=0.09, se=F) +  #AfD Party curve with span = 0.09 assigned the color purple
  geom_point(aes(x=end_date, y=fdp), colour=col_fdp, alpha=0.5) + #FDP Party points assigned the color yellow
  geom_smooth(aes(x=end_date, y=fdp), colour=col_fdp, span=0.09, se=F) + #FDP Party curve with span = 0.09 assigned the color yellow
  geom_point(aes(x=end_date, y=linke), colour=col_linke, alpha=0.5) + #Linke Party points assigned the color blue
  geom_smooth(aes(x=end_date, y=linke), colour=col_linke, span=0.09, se=F) + #Linke Party curve with span = 0.09 assigned the color blue
  geom_point(aes(x=end_date, y=grune), colour=col_grune, alpha=0.5) + #Grune Party points assigned the color green
  geom_smooth(aes(x=end_date, y=grune), colour=col_grune, span=0.09, se=F) + #Grune Party curve with span = 0.09 assigned the color green
  scale_y_continuous(breaks=seq(0, 40, 10), labels=c('0%', '10%', '20%', '30%', '40%')) + #Adjusting the scale
  geom_hline(yintercept=5, linetype='dashed', color='gray') + 
  scale_color_manual(name='',
                     labels = c('Union', 'SPD', 'AfD', 'FDP', 'Linke', 'Grüne'),
                     values = c('col_union'=col_union,'col_spd'=col_spd,'col_af_d'=col_af_d,'col_fdp'=col_fdp,'col_linke'=col_linke,'col_grune'=col_grune)) + #Creating a legend 
    labs(x='', y='') +
  theme_minimal() + #change theme to be more similar to the original
  theme(legend.background = element_rect(fill=NA, size=.3, linetype="solid")) + #add box around legend to be more similar to the original
  NULL

ggsave(file='elections_plot.png', plot=plot, width=18, height=8)

plot

7 Details

Who did you collaborate with: Hanlu Lin, Hao Ni, Junna Yanai, Lazar Jelic, Purva Sikri, Valeria Morales
Approximately how much time did you spend on this problem set: 7 hours
What, if anything, gave you the most trouble: Finding the filtering criteria of the election results in the first challenge, Showing a chart legend in the second challenge

8 Bibliography

Kohli, S. There are places on earth where and beer are cheaper than water. Retrieved September 4, 2021, https://qz.com/319919/these-are-the-places-on-earth-where-wine-and-beer-are-cheaper-than-water

Munns, S (2019). Employee Turnover Rates by Industry Comparison. Retrieved September 3, 2021, https://www.e-days.com/news/employee-turnover-rates-an-industry-comparison

Muster, Hans (2021) Recoil Source code

Nugent, P. What Namibia’s breweries tell us about the nation’s past. Retrieved September 4, 2021, https://qz.com/africa/1982787/how-namibian-beer-politics-and-identity-all-align

OECD/The World Bank (2020), Health at a Glance: Latin America and the Caribbean 2020, OECD Publishing, Paris, https://doi.org/10.1787/6089164f-en

Session 2: Homework 1

Study Group 10

2021-09-05

1 Where Do People Drink The Most Beer, Wine And Spirits?

2 Analysis of movies- IMDB dataset

2.1 Use your data import, inspection, and cleaning skills to answer the following:

2.2 Use `ggplot` to answer the following

3 Returns of financial stocks

4 On your own: IBM HR Analytics

5 Challenge 1: Replicating a chart

6 Challenge 2: Opinion polls for the 2021 German elections

7 Details

8 Bibliography

Session 2: Homework 1

Study Group 10

2021-09-05

1 Where Do People Drink The Most Beer, Wine And Spirits?

2 Analysis of movies- IMDB dataset

2.1 Use your data import, inspection, and cleaning skills to answer the following:

2.2 Use ggplot to answer the following

3 Returns of financial stocks

4 On your own: IBM HR Analytics

5 Challenge 1: Replicating a chart

6 Challenge 2: Opinion polls for the 2021 German elections

7 Details

8 Bibliography

2.2 Use `ggplot` to answer the following